A Dual-Store Structure for Knowledge Graphs

Zhixin Qi, Hongzhi Wang, Haoran Zhang

Abstract: To effectively manage increasing knowledge graphs in various domains, a hot research topic, knowledge graph storage management, has emerged. Existing methods are classified to relational stores and native graph stores. Relational stores are able to store large-scale knowledge graphs and convenient in updating knowledge, but the query performance weakens obviously when the selectivity of a knowledge graph query is large. Native graph stores are efficient in processing complex knowledge graph queries due to its index-free adjacent property, but they are inapplicable to manage a large-scale knowledge graph due to limited storage budgets or inflexible updating process. Motivated by this, we propose a dual-store structure which leverages a graph store to accelerate the complex query process in the relational store. However, it is challenging to determine <i>what</i> data to transfer from relational store to graph store at <i>what</i> time. To address this problem, we formulate it as a Markov Decision Process and derive a physical design tuner <inline-formula><tex-math notation="LaTeX">${{\sf DOTIL}}$</tex-math></inline-formula> based on reinforcement learning. With <inline-formula><tex-math notation="LaTeX">${{\sf DOTIL}}$</tex-math></inline-formula> , the dual-store structure is adaptive to dynamic changing workloads. Experimental results on real knowledge graphs demonstrate that our proposed dual-store structure improves query performance up to average 50.11 percent compared with the most commonly used relational stores.